THE JOURNAL OF FINANCE· VOL. LXXI, NO. 1 · FEBRUARY 2016

学术研究摧毁了股票收益的可预测性吗？

作者：R. David McLean, Jeffrey Pontiff

摘要：我们选取了97个被证明可以预测横截面股票收益的变量，研究它们的样本外和发表后收益的可预测性。样本外和发表后组合收益分别（比样本）低26%和58%。样本外收益的下降是对数据挖掘效应上限的刻画。我们估计，发表令（投资者）获知预测因子，然后进行交易，此时收益下降了32%（58%–26%）。对于样本内收益更高的预测因子，其发表后收益下降得欧洲杯网站_欧洲杯下注平台-官网推荐，且高异质风险和低流动性的股票组合拥有更高的回报。预测因子组合在发表后与其他已发表预测因子组合的相关性上升。我们的发现指出，投资者从学术发表中了解到了错误定价信息。

关键词：学术研究，股票收益，可预测性

Does Academic Research Destroy Stock Return Predictability?

R. David McLean, Jeffrey Pontiff

ABSTRACT

We study the out-of-sample and post-publication return predictability of 97 variables shown to predict cross-sectional stock returns. Portfolio returns are 26% lower out-of-sample and 58% lower post-publication. The out-of-sample decline is an upper bound estimate of data mining effects. We estimate a 32% (58%–26%) lower return from publication-informed trading. Post-publication declines are greater for predictors with higher in-sample returns, and returns are higher for portfolios concentrated in stocks with high idiosyncratic risk and low liquidity. Predictor portfolios exhibit post-publication increases in correlations with other published-predictor portfolios. Our findings suggest that investors learn about mispricing from academic publications.

Keywords: academic research, stock return, predictability

原文链接：http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2156623

翻译：任兆月